import xgboost as xgb
import pytest
import sys
import numpy as np

from xgboost import testing as tm

pytestmark = pytest.mark.skipif(**tm.no_sklearn())

rng = np.random.RandomState(1994)


def test_sycl_binary_classification():
    from sklearn.datasets import load_digits
    from sklearn.model_selection import KFold

    digits = load_digits(n_class=2)
    y = digits["target"]
    X = digits["data"]
    kf = KFold(n_splits=2, shuffle=True, random_state=rng)
    for cls in (xgb.XGBClassifier, xgb.XGBRFClassifier):
        for train_index, test_index in kf.split(X, y):
            xgb_model = cls(random_state=42, device="sycl", n_estimators=4).fit(
                X[train_index], y[train_index]
            )
            preds = xgb_model.predict(X[test_index])
            labels = y[test_index]
            err = sum(
                1 for i in range(len(preds)) if int(preds[i] > 0.5) != labels[i]
            ) / float(len(preds))
            print(preds)
            print(labels)
            print(err)
            assert err < 0.1